The latent structure of interpersonal problems: Validity of dimensional, categorical, and hybrid models.

Abstract

Interpersonal problems are key transdiagnostic constructs in psychopathology. In the past, investigators have neglected the importance of operationalizing interpersonal problems according to their latent structure by using divergent representations of the construct: (a) computing scores for severity, agency, and communion (“dimensional approach”), (b) classifying persons into subgroups with respect to their interpersonal profile (“categorical approach”). This hinders cumulative research on interpersonal problems, because findings cannot be integrated both from a conceptual and a statistical point of view. We provide a comprehensive evaluation of interpersonal problems by enlisting several large samples (Ns = 5,400, 491, 656, and 712) to estimate a set of latent variable candidate models, covering the spectrum of purely dimensional (i.e., confirmatory factor analysis using Gaussian and nonnormal latent t-distributions), hybrid (i.e., semiparametric factor analysis), and purely categorical approaches (latent class analysis). Statistical models were compared with regard to their structural validity, as evaluated by model fit (corrected Akaike’s information criterion and the Bayesian information criterion), and their concurrent validity, as defined by the models’ ability to predict relevant external variables. Across samples, the fully dimensional model performed best in terms of model fit, prediction, robustness, and parsimony. We found scant evidence that categorical and hybrid models provide incremental value for understanding interpersonal problems. Our results indicate that the latent structure of interpersonal problems is best represented by continuous dimensions, especially when one allows for nonnormal latent distributions.

Mapping established psychopathology scales onto the Hierarchical Taxonomy of Psychopathology (HiTOP)

“…Although the HiTOP-Map captured the majority of original scales with correlations of r > .70, some of the those were not adequately represented though. The insufficient coverage of interpersonal style dimensions (IIP-32 Agency and Communion) by the HiTOP-Map is consistent with the notion that interpersonal styles mostly reflect nonpathological personality traits (e.g., Wendt et al, 2019;Williams & Simms, 2016). Furthermore, DASI Drugs and Alcohol provided unique information that were not sufficiently covered by the HiTOP-Map Risk Taking scale, indicating that substance use conveys incremental information beyond the disposition towards taking unreasonable risks.…”

“…Data were collected as part of the "Probing Social Exchanges" project in which computational neuroscience is used to better understand Borderline and Anti-Social Personality Disorder. The data has been previously used to study various research questions distinct from the current research (Euler et al, 2019;Huang et al, 2020;Wendt et al, 2019). Participants were recruited in Greater London via the Personality and Mood Disorder Research Consortium consisting of 258 healthy community participants and 649 outpatients (N = 909; 66% female; mean age of 30.7, range = 16 -65, SD = 10.4) from NHS IAPT services for Mood Disorders and secondary or tertiary specialist services for personality disorders referred from National Health Service specialist personality disorder clinical services.…”

A Common Metric for Self-Reported Severity of Personality Disorder

“…Our results do not contradict but are fully consistent with such a perspective. Future studies may also explore the possibility of nonmonotonic item response functions (e.g., generalized graded unfolding models) [64] or nonnormal latent distributions (e.g., semiparametric factor analyses) [65], both of which seem to be plausible candidate models for the severity of PD.…”

Classification of probable online social networking addiction: A latent profile analysis from a large-scale survey among Chinese adolescents

“…Scree plots were used for visual examination (Masyn, 2013). A significant bootstrapping likelihood-ratio test (BLRT) result (P value <0.05) implies that the model with k classes fits better than that with k-1 classes; 2) Higher entropy values represent better classification accuracies (range from 0 to 1), and the values >0.8 imply high accuracies (Fonseca-Pedrero, Ortuno-Sierra, de Albeniz, Muniz, & Cohen, 2017); 3) All the latent classes identified by LPA should include at least 5% of the sample to eliminate fundamentally impractical solutions and prevent over fitting (Nagin, 2005;Wendt et al, 2019;Zhang, Zhang, Goyal, Mo, & Hong, 2018). There is no single criterion for deciding the number of latent classes, and the flow of the logic for decision was further explained in the Result section.…”